Accession Number : ADA117439
Title : Learning by Augmenting Rules and Accumulating Censors.
Descriptive Note : Memorandum rept.,
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Personal Author(s) : Winston,Patrick H
PDF Url : ADA117439
Report Date : May 1982
Pagination or Media Count : 25
Abstract : This paper is a synthesis of several sets of ideas: ideas about learning from precedents and exercises, ideas about learning using near misses, ideas about generalizing if-then rules, and ideas about using censors to prevent procedure misapplication. The synthesis enables two extensions to an implemented system that solves problems involving precedents and exercises and that generates if-then rules as a byproduct. These extensions are as follows: If-then rules are augmented by if-plausible conditions, creating augmented if-then rules. An augmented if-then rule is blocked whenever facts in hand directly deny the truth of an if-plausible condition. When an augmented if-then rule is used to deny the truth of an if-plausible condition, the rule is called a censor. Like ordinary augmented if-then rules, censors can be learned. Definition rules are introduced that facilitate graceful refinement. The definition rules are also augmented if-then rules. They work by virtue of if-plausible entries that capture certain nuances of meaning different from those expressible by necessary conditions. Like ordinary augmented if-then rules, definition rules can be learned. The strength of the ideas is illustrated by way of representative experiments. All of these experiments have been performed with an implemented system.
Descriptors : *Learning, *Reasoning, Artificial intelligence, Analogs, Mental ability, Perception(Psychology), Problem solving, Regulations, Synthesis, Censorship, Augmentation
Subject Categories : Psychology
Distribution Statement : APPROVED FOR PUBLIC RELEASE